Concrete carbonation prediction for varying environmental exposure conditions
Doctoral Thesis
2020
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The Durability Index (DI) approach has been developed in South Africa, in order to improve the durability performance of reinforced concrete structures. The DI approach is based on durability index tests, which are linked to transport mechanisms related to particular deterioration processes (Alexander et al., 1999a). Carbonation of concrete is governed, inter alia, by the microstructure and the transport characteristics of the concrete. A carbonation model with permeability coefficient (k) from the Oxygen Permeability Index (OPI) test as the key material variable was developed by Salvoldi (2010) using accelerated carbonation test data. The main aim of this research is to further develop the carbonation model by adopting the modelling framework of Salvoldi (2010) using natural carbonation data. For the experimental work, a total 48 different concrete mixes were produced by with different water: binder ratios (w/b), cement types, cement extender (addition) type and curing regime. The OPI test was conducted on all the concretes, and their corresponding permeability coefficients were determined. A set of 48 concrete specimens were exposed to five different sites for natural carbonation, and carbonation depths were measured periodically. Based on the modelling framework of Salvoldi (2010) and using the natural carbonation data between 150- 850 days, a model predicting the depth of natural carbonation was developed. However, in the case of concrete exposed to rain, drying/wetting is a major factor influencing the rate of carbonation. Therefore, the carbonation model was further modified taking into account the influence of drying/wetting cycles, by coupling it with a moisture model. For the development of the moisture model, the concrete specimens were exposed to a laboratory environment maintained at constant temperature and relative humidity (RH). The internal RH of the concrete specimens at varying depth was measured at different time intervals. Based on the measured RH data, the moisture model was also developed with ‘k' from the OPI test as the key input parameter. The moisture model was then coupled with the carbonation model developed. This provides an integrated and powerful solution for predicting carbonation of concrete both sheltered and exposed to rain by using only one main material input parameter ‘k', which is one of the major contributions of this research.
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Gopinath, R. 2020. Concrete carbonation prediction for varying environmental exposure conditions. . ,Faculty of Engineering and the Built Environment ,Concrete Materials and Structural Integrity Research Unit (CoMSIRU). http://hdl.handle.net/11427/32700